Optimising risk and income under climate variability in Northern Ghana : a bio-economic modelling approach : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Agribusiness, School of Agriculture and Environment, Massey University, Manawatu, New Zealand
| dc.confidential | Embargo : No | |
| dc.contributor.advisor | Ramilan, Thiagarajah | |
| dc.contributor.author | Ahiamadia, David | |
| dc.date.accessioned | 2024-09-13T02:58:51Z | |
| dc.date.available | 2024-09-13T02:58:51Z | |
| dc.date.issued | 2024-09-11 | |
| dc.description.abstract | Neglecting the potential threats posed by climate variability in northern Ghana during crop production may result in severe financial setbacks for farm households, hindering their ability to attain food security and reliable income goals. Northern Ghana, marked by its semi-arid climate, featuring a unimodal rainfall pattern and intense hot and dry weather conditions, faces unique crop farming challenges. Smallholder farmers in the region depend heavily on rain-fed agriculture, grappling with unpredictable temperatures and irregular precipitation. The absence of resources for implementing effective irrigation methods amplifies the vulnerability of these farmers to climate-induced crop failures. In recent times, the adoption of climate-smart technologies (CSTs) has gained prominence as a strategy to mitigate yield losses. These include practices such as changing planting dates (PD), implementing compartmental bunding (CB), mulching (M), and transplanting (TP). Given that, this research aims to minimise the adverse effect of climate-related risks on the economic conditions of farm households, the study first employs stochastic dominance modelling to identify the most risk-efficient CSTs. The stochastic modelling process utilised the AquaCrop model to simulate yield variations by incorporating climate and soil data specific to northern Ghana. Subsequently, from the Aquacrop derived yield variabilities, variations in income were generated using price data sourced from the Food and Agricultural Organization (FAO). The analysis revealed that changing planting date from April to May was the most risk-efficient choice for maize and sorghum under farmers’ practice and recommended practice. However, transplanting was the most risk-efficient technology for rice farming. The study also highlights the importance of considering the risk-averse nature of smallholder farmers when selecting CSTs. Further, although the stochastic dominance modelling was used to model income from different CSTs and their risk profiles, the approach does not consider the interaction between farmer resource endowments and preferences for risk management. Given that farming systems in the study area are heterogeneous, a multivariate statistical method was applied to 615 farm households using Principal Component Analysis (PCA) coupled with cluster analysis to generate farm types with similar characteristics. The farm household data was sourced from the Africa Rising Baseline Evaluation Survey for northern Ghana. The variables selected for the study were classified based on the following criteria: resource endowments, production goals, climate risk, demographics, production intensity, expenditure, and level of inputs. Eventually, farm types one, two, and three were identified as most resource-endowed, moderately resource-endowed, and less resource-endowed, respectively. This study further employs a novel weighting system to address the level of importance farmers attach to their risk and income objectives. Due to the limitations in resource endowments, all farm types attached more weight, albeit differently, to low-risk, less-income-generating activities as their climate risk management strategy. Against this backdrop, many risk-averse smallholder households are willing to compromise by accepting lower returns in exchange for a more stable income. To achieve this, the study analyses the interactive effect of socio-economic and biophysical risk sources on the income-risk trade-off decisions of farm typologies by employing a mixed integer quadratic compromise risk programming model. The model was developed from three theories: Compromise programming, Quadratic programming, and Linear expenditure system. By generating an income-risk frontier, the model proposes a combination of crops farmers could grow to minimise risk and obtain a stable income. Further, the model analyses the implications of non-separability in consumption and production on each farm type's risk and income trade-offs and expenditure patterns. Also, farm-type wealth effects on crop choices were determined by the weight farm households attached to their risk and income objectives. As a result, the findings of this study indicate that to achieve a stable income, relatively poor households should focus on growing maize (1.07 Ha) with rice (0.68 Ha), sorghum (0.23 Ha) and groundnut (0.02 Ha), while wealthier households grow rice (2.16 Ha), plus sorghum (0.98 Ha), and groundnuts (0.36 Ha). Moderately wealthy farmers should grow maize (0.50 Ha), rice (0.36 Ha), sorghum (0.29 Ha), and groundnut (0.05 Ha). Given the heterogeneous nature of smallholder farming systems in northern Ghana, this research approach is very useful for household-level income-risk decision-making under climate risk. | |
| dc.identifier.uri | https://mro.massey.ac.nz/handle/10179/71451 | |
| dc.publisher | Massey University | |
| dc.rights | The Author | |
| dc.subject | bio-economic modelling, climate variability, crops, income, mathematical programming, risk, smallholder farmers, variability | |
| dc.subject.anzsrc | 300208 Farm management, rural management and agribusiness | |
| dc.title | Optimising risk and income under climate variability in Northern Ghana : a bio-economic modelling approach : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Agribusiness, School of Agriculture and Environment, Massey University, Manawatu, New Zealand | |
| thesis.degree.discipline | Agribusiness | |
| thesis.degree.name | Doctor of Philosophy (PhD) | |
| thesis.description.doctoral-citation-abridged | In his thesis, David develops a multi-objective non-linear mathematical programming algorithm to optimise income and risk by recommending alternative cropping patterns subject to farm household resource constraints and risk preferences. His thesis as a whole makes an original contribution to the economic understanding of dryland agriculture and the use of bioeconomic models to improve decision-making in the context of climate variability. | |
| thesis.description.doctoral-citation-long | Northern Ghana, marked by its semi-arid climate, featuring a unimodal rainfall pattern and intense hot and dry weather conditions, faces unique crop farming challenges resulting in financial setbacks among smallholder farmers. To address this challenge, David developed a mixed integer quadratic compromise risk programming model. The model employs a multi-objective non-linear mathematical programming algorithm to analyse farm household income and risk decisions by optimising income and climate risk through the development of alternative cropping patterns subject to farm household resource constraints and risk preferences. His thesis as a whole makes an original contribution to the economic understanding of dryland agriculture and the use of bioeconomic models to improve decision-making in the context of climate variability. | |
| thesis.description.name-pronounciation | DEY-VID A-HEA-MA-DEA |
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